Machine Learning Potentials: A Roadmap Toward Next-Generation Biomolecular Simulations

• URL: http://arxiv.org/abs/2408.12625v1
• Date: Sat, 17 Aug 2024 07:53:33 GMT
• Title: Machine Learning Potentials: A Roadmap Toward Next-Generation Biomolecular Simulations
• Authors: Gianni De Fabritiis,
• Abstract summary: Machine learning potentials offer a revolutionary, unifying framework for molecular simulations across scales, from quantum chemistry to coarse-grained models. I discuss key challenges that must be addressed to fully realize their transformative potential in chemical biology and related fields.
• Score: 4.169915659794567
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine learning potentials offer a revolutionary, unifying framework for molecular simulations across scales, from quantum chemistry to coarse-grained models. Here, I explore their potential to dramatically improve accuracy and scalability in simulating complex molecular systems. I discuss key challenges that must be addressed to fully realize their transformative potential in chemical biology and related fields.

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